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This paper presents an optimal proposed allocating procedure for hybrid wind energy combined with proton exchange membrane fuel cell (WE/PEMFC) system to improve the operation performance of the electrical distribution system (EDS). Egypt has an excellent wind regime with wind speeds of about 10 m/s at many areas. The disadvantage of wind energy is its seasonal variations. So, if wind power is to supply a significant portion of the demand, either backup power or electrical energy storage (EES) system is needed to ensure that loads will be supplied in reliable way. So, the hybrid WE/PEMFC system is designed to completely supply a part of the Egyptian distribution system, in attempt to isolate it from the grid. However, the optimal allocation of the hybrid units is obtained, in order to enhance their benefits in the distribution networks. The critical buses that are necessary to install the hybrid WE/ PEMFC system, are chosen using sensitivity analysis. Then, the binary Crow search algorithm (BCSA), discrete Jaya algorithm (DJA) and binary particle swarm optimization (BPSO) techniques are proposed to determine the optimal operation of power systems using single and multi-objective functions (SOF/MOF). Then, the results of the three optimization techniques are compared with each other. Three sensitivity factors are employed in this paper, which are voltage sensitivity factor (VSF), active losses sensitivity factor (ALSF) and reactive losses sensitivity factor (RLSF). The effects of the sensitivity factors (SFs) on the SOF/MOF are studied. The improvement of voltage profile and minimizing active and reactive power losses of the EDS are considered as objective functions. Backward/forward sweep (BFS) method is used for the load flow calculations. The system load demand is predicted up to year 2022 for Mersi-Matrouh City as a part of Egyptian distribution network, and the design of the hybrid WE/PEMFC system is applied. The PEMFC system is designed considering simplified mathematical expressions. The economics of operation of both WE and PEMFC system are also presented. The results prove the capability of the proposed procedure to find the optimal allocation for the hybrid WE/PEMFC system to improve the system voltage profile and to minimize both active and reactive power losses for the EDS of Mersi-Matrough City.

Distributed generations (DGs) consider an important issue for the electric utilities as DGs relieve capacity constraints on the generation, transmission and distribution systems and obviate the need to build new facilities [_{2}) as a fuel [

generated from the wind farm can be used to produce H_{2} from the electrolysis of water in an electrolyzer, H_{2} can be stored in H_{2} tanks and converted to electrical energy through the PEMFC, for power generation at deficit times [_{2} generation and battery for storage purpose is designed and simulated in [

This paper proposes an efficient optimization procedure based on three SFs parallel with three different optimization techniques. First, the SFs will be used to select the candidate system buses for the installation of the hybrid WE/PEMFC system to reduce the search space of the optimization techniques. Second, the three different optimization techniques BCSA, DJA and BPSO are applied to find the optimal allocation of the hybrid WE/PEMFC system by achieving SOF/MOF. Third, the obtained results are compared with each other. The objective functions aim to minimize total active and reactive power losses and to improve voltage profile.

Voltage sensitivity factor (VSF), active losses sensitivity factor (ALSF) and reactive active losses sensitivity factor (RLSF) are presented in this paper to rank the system buses according to their severity, to detect the candidate system buses that will need to install the hybrid WE/PEMFC system.

● VSF is represented by the ratio of the magnitude of the base case voltage to the minimum limit of voltage (0.95 pu) as follow [

VSF = | V B | / 0.95 (1)

The active power losses (APLs) of the distribution line connecting between buses p and q can be formulated as follow:

P p q = P 2 + Q 2 V q 2 ⋅ R p q (2)

where; P is the total effective active power supplied beyond the bus q, Q is the total effective reactive power supplied beyond the bus q, R_{pq} is the resistance of the distribution line connecting between buses p and q and V_{q} is the voltage at bus q.

● ALSF can be written as:

ALSF = ∂ P p q ∂ P = ( 2 ∗ P ∗ R p q ) / V q 2 (3)

The reactive power losses (RPLs) of the distribution line connecting between buses p and q can be formulated as follows:

Q p q = P 2 + Q 2 V q 2 ⋅ X p q (4)

where; X_{pq} is the reactance of the distribution line connecting between buses p and q.

● RLSF can be written as:

RLSF = ∂ Q p q ∂ P = ( 2 ∗ P ∗ X p q ) / V q 2 (5)

The system buses that have the smallest VSF positive values are considered as the candidate buses that need to install the hybrid system, and will be placed at the top of the VSF list. While, the buses that have the largest ALSF and RLSF positive values are considered as the candidate buses that need to install the hybrid system, where these buses will be placed at the top of the ALSF list and RLSF list, respectively. The candidate system buses for the installation of the hybrid system based on the VSF list, ALSF list and RLSF list are inserted as control variables in the optimization algorithms.

The proposed optimization techniques aim to determine the best allocation of the hybrid WE/PEMFC system in the EDS of Mersi-Matrouh City, by minimizing bus voltage index (BVI), active losses index (ALI) and reactive losses index (RLI) as SOF and MOF.

By taking the ratio of a measure of an attribute with and without the hybrid WE/PEMFC system at the same loading conditions, some indices can be derived for any attribute as follows:

1) Minimizing BVI:

F 1 = M i n B V I % = | V B − V B , H | / V B (6)

where; V_{B} and V_{b}_{,H} are the system bus voltage without and with the hybrid WE/ PEMFC system, respectively.

2) Minimizing ALI:

F 2 = M i n A L I % = ( A L S K , H / T A L s y s ) (7)

where, AL_{sk}_{,H} is the APLs of section k of the EDS with considering the hybrid WE/PEMFC system. TAL_{sys} is the total active power losses (TAPLs) of the EDS without considering the hybrid WE/PEMFC system.

3) Minimizing RLI:

F 3 = M i n R L I % = ( R L S K , H / T R L s y s ) (8)

where, RL_{sk}_{,H} is the RPLs of section k of the EDS with considering the hybrid WE/PEMFC system. TRL_{sys} is the total reactive power losses (TRPLs) of the EDS without considering the hybrid WE/PEMFC system.

The MOF is formulated as:

F = M i n M O F = W 1 ⋅ F 1 + W 2 ⋅ F 2 + W 3 ⋅ F 3 (9)

with 0 ≤ W i ≤ 1 , ∑ i = 1 3 W i = 1

where; W_{1},W_{2} and W_{3} are the weighting factors (WFs) for BVI%, ALI% and RLI%, respectively. And can be obtained by trial and error.

The SOF/MOF are subjected to the following system constraints:

1) System bus voltage constraints

The voltage at each bus V_{B} must be within their minimum and maximum limits (V_{bmin} and V_{bmax}) as:

V B M I N ≤ V B ≤ V B M A X (10)

2) Total number of hybrid units (DGs) constraint

The number of DGs (N_{DGs}) must be less than or equal to the maximum permissible number of DGs (N_{DGmax}).

N D G s ≤ N D G m a x (11)

3) Active power flow constraint

The active power flow in the k^{th} line (AF^{K}) should be less than or equal to the maximum permissible active power flow (AF^{Kmax}).

A F K ≤ A F K m a x (12)

4) Reactive power flow constraint

The reactive power flow in line k (RF^{K}) should be less than or equal to the maximum permissible reactive power flow (RF^{Kmax}).

R F K ≤ R F K m a x (13)

The BFS method is one of the most common ways used for load flow calculation of distribution system as it is fast, simple, robust convergence and low memory requirement for processing with efficiencies and solution accuracies computational [

In this paper, three optimization techniques are used to determine the best allocation of the hybrid WE/PEMFC system in the EDS of Mersi-Matrouh City. These optimization techniques are the BCSA, DJA and BPSO.

CSA is a nature-inspired algorithm presented by Askarzadeh in 2016 [

● Initialization: the Crow in the folk represents a solution to the problem.

● If there is a solution space with dimension d has a Crow folk of N Crows, the position X of Crowi at iteration t can be expressed by the vector:

X i , t = [ X 1 i , t X 2 i , t X 3 i , t ⋯ X d i , t ]

● Each Crow has a memory to store the best position of its stored food source.

● The memoryM of all Crows at iteration t for dimension d are initialized as follows:

M = [ m 11 t ⋯ m 1 d t ⋮ ⋱ ⋮ m N 1 t ⋯ m N d t ]

● Solution evaluation (fitness): each solution in the population is evaluated using objective function.

● Position update: to update the position of the Crow between Crows J and i, there are two cases:

Case 1: Crow J does not recognize that Crow i is going after it. Hence, Crow i will get close the storing place of Crow J.

Case 2: Crow J recognizes that Crow i is going after it, so, it will move to another position to deceive Crow i and to save its food.

X i , t + 1 = { X i , t + r a i × F l i , t × ( m j , t − X i , t ) r J ≥ A P J , t randomposition otherwise

where; AP is the awareness factor and Fl^{i}^{,t} is the flight length of Crow i at iteration t. The main reasons of using CSA are for easy implementation, few control parameters to adjust, fast convergence speed and high efficiency. To further enhance the performance of the classical CSA, BCSA is proposed. At each position, the solution considers values “0” or “1”, where, “0” represents the solution that is not selected and “1” represents the solution that is selected.

Jaya algorithm is one of the newly developed population based optimization method, which was proposed by Rao in 2016 [

The mechanics of PSO technique are inspired by the swarming of biological populations. In PSO technique, the particles change their positions with time [

The optimal allocation procedure is illustrated below:

1) Input data: input the number of system buses, load demand at each bus, transmission lines’ impedance, control parameters of the used optimization technique.

2) Perform the initial load flow analysis using the BFS method to calculate the SFs, the VSF are arranged in ascending order for all buses of the study system. While, the ALSF and RLSF are arranged in descending order for all system lines. Thus, the candidate buses for inserting the hybrid WE/PEMFC system can be determined.

3) Implement one of the three proposed optimization techniques.

4) Perform the load flow analysis using BFS method again to obtain the voltage at each system bus, APLs and RPLs.

5) Calculate the fitness function of SOF/MOF for each proposed solution.

6) Repeat until the maximum number of iterations is reached or the solution is obtained.

7) The previous steps will be repeated with the other two proposed optimization techniques. Then the obtained results are compared with each other.

A hybrid WE/PEMFC system is designed, in which the surplus power from the wind farm is used to generate H_{2} through the water electrolyzer, the H_{2} will be stored in the H_{2} tank, then, H_{2} will be used to generate the required electricity through the PEMFC to supply loads at deficit times, as so, the loads will be supplied in reliable way, as will be illustrated in the following sections.

Different wind turbines (WTs) which are suitable for supplying the EDS were optimized considering both technical and economical points of view [

The average output of WT is given as follows [

P a v e = exp [ − ( V c i n / C ) K ] − exp [ − ( V r / C ) K ] [ ( V r / C ) K − ( V c i n / C ) K ] − exp [ − ( V c o u t / C ) K ] ⋅ P r (14)

where; P_{ave} is the average power, P_{r} is the rated power, V_{cin} is the cut-in wind speed, V_{r} is the rated wind speed and V_{cout} is the cut-out wind speed, C and K are the scale and shape parameters of Weibull probability distribution function, respectively. The average output of the wind farm is given as follows:

P A , W F = P a v e × N D G s (15)

The unit energy cost (U_{E.C}) of WT is calculated dependent on the value of both the annual capital cost (AC.C) and the annual operation cost (AO.C). The U_{E.C} is given as:

U E . C = ( A C . C + A O . C ) / A E W T $ / year (16)

where; AE_{WT} is the energy output of WT per year. The total unit energy cost of the wind farm (U_{WF}) is given as:

U W F = U E . C × N D G s + P L a n d $ / year (17)

where; P_{Land} is the price of land used for the installation of the wind farm.

The amount of H_{2} that needs to be supplied the FC to produce one kWh of electricity from the surplus power of wind farm can be obtained as follow [

H 2 A m o u n t = ( 2 ∗ n r a t e ∗ 3600 ) / ( ( 237.2 n r a t e ) ∗ 10 − 3 ) gram / kWh (18)

where, n_{rate} is the rate of flow of H_{2} into the cell in (mol/sec).

In this paper, PEMFC is designed at low operating temperature between [60˚C - 100˚C] that has relatively high electrical efficiency between [40% - 50%]. The design of PEMFC is performed by considering the voltage-current electrical characteristics of the PEMFC as shown in

V c e l l = 0.85 − 0.25 ∗ ( I / A ) (19)

where, V_{cell}, I and A are the single cell voltage, the current and the single cell area, respectively.The stack of the PEMFC can be expressed as follow

V s t a c k = ( 0.85 − 0.25 ∗ ( I / A ) ) ∗ N (20)

P s t a c k = V s t a c k ∗ I (21)

where; V_{stack}, P_{stack}and N are the stack voltage, the power of the stack and the number of stack series cells, respectively.

The electrolyzer size is determined by its production flow rate (H_{2}) as illustrated [

H 2 r a t e = ( 60 ∗ E L ∗ n e ∗ g m ) / ( U G ∗ V c e l l ∗ n e m ∗ n m m ) (22)

where; H_{2rate} is the H_{2} flow rate, E_{L} is the average electrical load in Wh/day, n_{e} is the number of electrons per second for 1 ampere, g_{m} is the molar mass of H_{2} in g/mol, U_{f} is the utilization factor which takes 0.8, V_{cell} is the cell voltage, n_{em} is the number of electrons per each molecule of H_{2} and n_{mm} is the number of molecules per H_{2} mol. The electrolyzer size can be calculated by dividing the total amount of H_{2} per year required from the PEMFC by the total windy hours per year as follow:

E S i z e = ( H 2 r a t e ∗ E L ∗ 365 ) / h W i n d (23)

where; h_{wind} is the total windy hours per year.

The volume of the H_{2} tank can be determined from the ideal gas low as follow [

V T = ( n ∗ R ∗ T ) / P (24)

where; V_{T} is the volume of the H_{2} tank, n is the number of moles, R is the gas constant, T is the temperature in Kelvin and P is pressure.

The annual cost (AC) of PEMFC system can be calculated as follow:

A C = C C + C T a n k + C E l e c t r o + C O & M + ( C r u n / ( 1 + i ) Y ) − ( S v / ( 1 + i ) Y ) (25)where, C_{O&M} is the annual operating and maintenance costs of the PEMFC system, Y is the life time of the PEMFC system, C_{C} is the capital cost, C_{Tank} is the annual cost of H_{2} tank, C_{Electro} is the annual cost of the electrolyzer, S_{v} is the annual salvage value, C_{run} is the annual running cost and i is the interest rate. The unit energy cost (C_{OE}) can be calculated by dividing the AC of the PEMFC system by the total generated energy, during the system cycle as follow:

where, E^{e} is the electrical generated energy in kWh.

The optimal hybrid WE/PEMFC system approach discussed in Section 3 is applied for supplying the EDS of Mersi-Matrouh City, in attempt to isolate it from the grid. Also, the proposed optimal allocation procedure of the hybrid WE/ PEMFC system in the EDS of Mersi-Matrouh City discussed in Section 2 is applied.

Load forecasting of the electrical peak load demand of Mersi-Matrouh City is obtained up to year 2022 [_{WT}). The hybrid WE/PEMFC system consists of 45 × 3000 kW-WT, which gives 239,611.5 MWh per year. The wind farm gives surplus power of about 427.999 MWh which is used to produce 7,275,983 grams of H_{2} through an electrolyzer, where, QLC-180 PEM electrolyzer cell stack is used [

The proposed economical model in section 3.1.2 is applied to calculate the U_{E.C} and U_{WF} up to year 2022, as shown in

● The capital cost (C.C) is assumed as 800 $/kW.

● The annual operation cost (AO.C) is assumed as 2.0 ¢/kWh.

2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | Year |
---|---|---|---|---|---|---|---|---|

4982 | 4656 | 4351 | 4066 | 3800 | 3552 | 3319 | 3102.6 | P_{D} (MW) |

- | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | Year |

- | 8000 | 7477 | 6987 | 6530 | 6103 | 5704 | 5331 | P_{D} (MW) |

427.999 | Wind Farm Surplus Power (MWh) |
---|---|

7,275,983 | Grams of H_{2} Produced From the Surplus Power (g) |

107.88 | Produced Electricity From PEMFC System (MWh) |

67.6 | Wind Farm Deficit Power (MWh) |

C.C ($) | AC.C ($) | AO.C ($) | U_{E.C} (¢/kWh) | U_{WF} (¢/kWh) |
---|---|---|---|---|

2,400,000 | 321,120 | 106,494 | 8 | 8.035 |

● The interest rate is assumed as 12%, while, the life-time is assumed as 20 years.

● The price of land used for wind farm is equal to 40,060$ per year.

PEMFC system is designed as illustrated in Section 3.2 and the design parameters of the PEMFC stacks are illustrated in _{cell} = 0.6V, V_{stack} = 72V, electorlyzer efficiency is 85% and PEMFC efficiency is 45% as Equations (19)-(24).

The C_{OE} of the designed PEMFC system is also calculated including H_{2} tank cost and electrolyzer cost as shown in

● The C_{C} of the PEMFC system is 4400$/kW.

● C_{O&M} is assumed as 0.035$/kW.

● i is assumed as 5%.

● C_{run} and S_{v} are assumed as 9% of the C_{C}.

To reduce the storage volume of the H_{2} gas, the pressure needs to increase. In this paper, the type four (type IV) tank is used [

The proposed procedures are applied to the EDS of Mersi-Matrouh City in order to solve the optimal allocation of hybrid WE/PEMFC system problem by achieving the SOF/MOF and satisfying system constraints. Optimal allocating problem is solved with and without considering the SFs. Then these results are compared with each other.

Electrolyzer Type | No.Stacks | H_{2} Tank Type | V_{cell} (V) | I (A) | V_{stack} (V) | P_{stack} (W) |
---|---|---|---|---|---|---|

QLC-180 | 8 | Type IV, 200 Liter, 500 bar | 0.6 | 69.44 | 72 | 5000 |

C_{OE} ($/kWh) | C_{O}_{&M} ($) | C_{C} ($) |
---|---|---|

0.27 | 4200 | 32,742.85 |

Minimizing the SOF is performed using the three proposed optimization techniques without and with considering the SFs.

_{1} without and with considering the SFs using the three proposed optimization techniques in year 2022. From

F_{1} | F_{2} | F_{3} | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Bus. No | BCSA | DJA | BPSO | BCSA | DJA | BPSO | BCSA | DJA | BPSO | |||||||||

S | N | S | N | S | N | S | N | S | N | S | N | S | N | S | N | S | N | |

2 | 2 | 4 | 5 | 9 | 5 | 9 | 2 | 4 | 7 | 12 | 6 | 10 | 2 | 4 | 7 | 12 | 6 | 10 |

3 | 5 | 9 | 2 | 4 | 2 | 4 | 6 | 10 | 2 | 4 | 6 | 10 | 6 | 10 | 2 | 4 | 6 | 10 |

4 | 2 | 4 | 2 | 4 | 3 | 5 | 2 | 4 | 3 | 5 | 3 | 5 | 2 | 4 | 3 | 5 | 3 | 5 |

5 | 3 | 5 | 2 | 4 | 3 | 5 | 4 | 7 | 2 | 4 | 2 | 4 | 4 | 7 | 2 | 4 | 2 | 4 |

6 | 5 | 9 | 4 | 7 | 3 | 5 | 7 | 12 | 6 | 10 | 5 | 9 | 7 | 12 | 6 | 10 | 5 | 9 |

7 | 3 | 5 | 3 | 5 | 3 | 5 | 4 | 7 | 4 | 7 | 3 | 5 | 4 | 7 | 4 | 7 | 3 | 5 |

8 | 2 | 4 | 2 | 4 | 3 | 5 | 2 | 4 | 2 | 4 | 4 | 7 | 2 | 4 | 2 | 4 | 4 | 7 |

9 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 | 3 | 5 | 2 | 4 | 2 | 4 | 3 | 5 |

10 | 2 | 4 | 1 | 2 | 2 | 4 | 2 | 4 | 2 | 4 | 4 | 7 | 2 | 4 | 2 | 4 | 4 | 7 |

11 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 |

12 | 2 | 4 | 3 | 5 | 2 | 4 | 3 | 5 | 4.9 | 9 | 3 | 5 | 3 | 5 | 4.9 | 9 | 3 | 5 |

13 | 3 | 5 | 3 | 5 | 3 | 5 | 4 | 7 | 4.9 | 9 | 3 | 5 | 4 | 7 | 4.9 | 9 | 3 | 5 |

14 | 2 | 4 | 2 | 4 | 2 | 4 | 3 | 5 | 2 | 4 | 4 | 7 | 3 | 5 | 2 | 4 | 4 | 7 |

15 | 2 | 4 | 2 | 4 | 2 | 4 | 3 | 5 | 3 | 5 | 4 | 7 | 3 | 5 | 3 | 5 | 4 | 7 |

16 | 2 | 4 | 2 | 4 | 2 | 4 | 3 | 5 | 3 | 5 | 2 | 4 | 3 | 5 | 3 | 5 | 2 | 4 |

F_{1} | F_{2} | F_{3} | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Bus. No | BCSA | DJA | BPSO | BCSA | DJA | BPSO | BCSA | DJA | BPSO | |||||||||

S | N | S | N | S | N | S | N | S | N | S | N | S | N | S | N | S | N | |

2 | - | - | - | - | - | - | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 |

3 | - | - | - | - | - | - | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 |

4 | - | - | - | - | - | - | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 |

5 | 3 | 5 | 2 | 4 | 2 | 4 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 |

6 | - | - | - | - | - | - | 4 | 7 | 2 | 4 | 4 | 7 | 4 | 7 | 2 | 4 | 4 | 7 |

7 | - | - | - | - | - | - | 3.2 | 6 | 3.2 | 6 | 3.2 | 6 | 3.2 | 6 | 3.2 | 6 | 3.2 | 6 |

8 | - | - | - | - | - | - | 3.3 | 6 | 3.3 | 6 | 3.3 | 6 | 3.3 | 6 | 3.3 | 6 | 3.3 | 6 |

9 | 2 | 4 | 2 | 4 | 2 | 4 | 3.2 | 6 | 3.2 | 6 | 3.2 | 6 | 3.2 | 6 | 3.2 | 6 | 3.2 | 6 |

10 | - | - | - | - | - | - | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 | 2.9 | 5 |

11 | - | - | - | - | - | - | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 |

12 | 2 | 4 | 3 | 5 | 2 | 4 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 | 2.7 | 5 |

13 | - | - | - | - | - | - | 3.3 | 6 | 3.3 | 6 | 3.3 | 6 | 3.3 | 6 | 3.3 | 6 | 3.3 | 6 |

14 | - | - | - | - | - | - | 3.1 | 6 | 3.1 | 6 | 3.1 | 6 | 3.1 | 6 | 3.1 | 6 | 3.1 | 6 |

15 | 2 | 4 | 2 | 4 | 2 | 4 | 3.1 | 6 | 3.1 | 6 | 3.1 | 6 | 3.1 | 6 | 3.1 | 6 | 3.1 | 6 |

16 | 2 | 4 | 2 | 4 | 3 | 5 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 |

With Inserting the Hybrid WE/PEMFC System | Without Inserting the Hybrid WE/PEMFC System | Items | |||||
---|---|---|---|---|---|---|---|

BPSO | DJA | BCSA | |||||

With SFs | Without SFs | With SFs | Without SFs | With SFs | Without SFs | ||

1.8 | 9.73 | 0.82 | 8.51 | 0.89 | 9.45 | 34.13 | F_{1} (%) |

20.91 | 24.47 | 20.73 | 22.96 | 20.91 | 22.8 | 100 | F_{2} (%) |

20.68 | 23.82 | 20.45 | 22.21 | 20.68 | 22.88 | 100 | F_{3} (%) |

94.7 | 71.5 | 97.5 | 75 | 97.3 | 72.3 | - | Voltage Improvement (%) |

79.09 | 75.5 | 79.27 | 77 | 79.09 | 77.2 | - | Active Power Losses Saving (%) |

79.32 | 76 | 79.55 | 77.8 | 79.32 | 77.12 | - | Reactive Power Losses Saving (%) |

DJA when considering the SFs that improves the bus voltage about 97.5%, then, BCSA improves the bus voltage by 97.3%, finally BPSO technique improves the bus voltage by 94.7%. There is a clear improving in the value of F_{1} when considering the effect of the SFs as F_{1} is increased from 75% to 97.5% using DJA. _{1} as SOF, respectively.

_{2} when considering the SFs that the saving in APLs is increased from 77% to 79.27 using DJA.

_{3} without and with considering the SFs using the three proposed optimization techniques, the best technique without considering the SFs is DJA that gives the highest saving in reactive power losses (RPLs) by 77.8% then, BCSA saves the RPLs by 77.12%, finally BPSO technique saves the RPLs by 76%. Also, the best technique, when considering the SFs, is DJA that gives the highest saving in RPLs by 79.55%, then, both BCSA and BPSO give the same saving in RPLs by 79.32%. There is a significant improving

in the value of F_{3} after taking the SFs into consideration which the saving in RPLs is increased from 77.8% to 79.55% using DJA.

MOF are dependent on the values of WFs, which takes the value of W_{1} as 0.5 to consider the voltage improvement is the first priority, while, the values of W_{2} and W_{3} take different values between [0 to 0.5 by step 0.05]. However, the values of W_{2} and W_{3} have taken as 0.45 and 0.05, respectively, to optimize the MOF using both BCSA and BPSO technique without and with considering the SFs. While, W_{2} and W_{3} are taken as 0.05 and 0.45, respectively, to optimize the MOF using DJA without and with considering the SFs.

BPSO | DJA | BCSA | Bus. No | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

With SFs | Without SFs | With SFs | Without SFs | With SFs | Without SFs | |||||||

N | S | N | S | N | S | N | S | N | S | N | S | |

5 | 2.9 | 7 | 4 | 5 | 2.9 | 12 | 7 | 5 | 2.9 | 4 | 2 | 2 |

5 | 2.9 | 10 | 6 | 5 | 2.9 | 2 | 1 | 5 | 2.9 | 10 | 6 | 3 |

4 | 2 | 4 | 2 | 5 | 2.7 | 4 | 2 | 5 | 2.7 | 4 | 2 | 4 |

5 | 2.7 | 7 | 4 | 4 | 2 | 4 | 2 | 5 | 2.7 | 7 | 4 | 5 |

5 | 3 | 9 | 5 | 2 | 1 | 12 | 7 | 5 | 3 | 12 | 7 | 6 |

6 | 3.2 | 4 | 2 | 6 | 3.2 | 9 | 4.9 | 6 | 3.2 | 7 | 4 | 7 |

6 | 3.3 | 9 | 5 | 6 | 3.3 | 4 | 2 | 6 | 3.3 | 4 | 2 | 8 |

6 | 3.2 | 5 | 3 | 6 | 3.2 | 4 | 2 | 6 | 3.2 | 4 | 2 | 9 |

5 | 2.9 | 5 | 3 | 5 | 2.9 | 4 | 2 | 5 | 2.9 | 4 | 2 | 10 |

5 | 2.7 | 5 | 3 | 5 | 2.7 | 4 | 2 | 4 | 2 | 4 | 2 | 11 |

5 | 3 | 4 | 2 | 5 | 2.7 | 9 | 4.9 | 4 | 2 | 5 | 3 | 12 |

6 | 3.3 | 7 | 4 | 6 | 3.3 | 9 | 4.9 | 6 | 3.3 | 7 | 4 | 13 |

6 | 3.1 | 4 | 2 | 6 | 3.1 | 4 | 2 | 6 | 3.1 | 5 | 3 | 14 |

6 | 3.1 | 5 | 3 | 6 | 3.1 | 4 | 2 | 6 | 3.1 | 5 | 3 | 15 |

4 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 4 | 2 | 5 | 3 | 16 |

With Inserting the Hybrid WE/PEMFC System | Without Inserting the Hybrid WE/PEMFC System | Items | |||||
---|---|---|---|---|---|---|---|

BPSO | DJA | BCSA | |||||

With SFs | Without SFs | With SFs | Without SFs | With SFs | Without SFs | ||

17.32 | 20.31 | 17.22 | 18.07 | 17.37 | 19.02 | 67.07 | MOF (%) |

0.2609 | 0.313 | 0.2601 | 0.3109 | 0.2612 | 0.2817 | 1.2348 | TAPLs (MW) |

0.3249 | 0.3812 | 0.3231 | 0.3892 | 0.3255 | 0.3529 | 1.5421 | TRPLs (MVAR) |

In this paper, a proposed hybrid WE/PEMFC system includes WTs and PEMFC has been efficiently presented. Proposed economical and generation models of 3000 kW-WT had been presented. Also, the economics of both wind energy and the PEMFC system had been discussed in year 2022. The BFS method has been used for the load flow calculation. A two-stage procedure has been introduced to allocate a hybrid WE/PEMFC system in the EDS for Mersi-Matrough City. In the first stage, the sensitivities analysis using three SFs have been presented to select the candidate buses for locating the hybrid system. While, in the second stage, the BCSA, DJA and BPSO techniques have been investigated to find the optimal allocation of the hybrid system considering the improvement of voltage profile and the minimization of both APLs and RPLs as objective functions, while system constraints are fully satisfied. Best optimization results have been obtained using DJA compared to BCSA and BPSO technique. Also, the proposed procedure using DJA considering MOF gives the best results in compared with the other two techniques. By considering the effect of SFs, the improvement in the value of the F_{1} is increased; however, the hybrid system is located at only 5 candidate system buses instead of the 16 buses. So, these SFs decrease the search space for optimization techniques and improve the voltage profile. Also, there are more significant saving in both F_{2} and F_{3} when the hybrid system are located at the candidate buses. The proposed procedure represents a potential tool to improve bus voltage magnitude, reduce both APLs and RPLs and helps their operators in smart grid environment.

The authors declare no conflicts of interest regarding the publication of this paper.

El-Ela, A.A.A., Allam, S.M. and Shehata, N.K. (2021) Optimal Allocation of a Hybrid Wind Energy-Fuel Cell System Using Different Optimization Techniques in the Egyptian Distribution Network. Energy and Power Engineering, 13, 17-40. https://doi.org/10.4236/epe.2021.131002

The key features of the QLC PEM electrolyzer used in this paper is illustrated in TableA1. While, the specification of type four hydrogen tank is illustrated in TableA2.

QLC-1000 | QLC-500 | QLC-300 | QLC-240 | QLC-180 | QLC-120 | QLC-60 | Model |
---|---|---|---|---|---|---|---|

4 | 2 | 1 | 4 | 3 | 2 | 1 | Cells |

138 | 138 | 138 | 85 | 85 | 85 | 85 | Stack Diameter (mm) |

0 - 1000 | 0 - 500 | 0 - 300 | 0 - 240 | 0 - 180 | 0 - 120 | 0 - 60 | H_{2} Flow Rate (mL/min) |

3591 | 1934 | 1487 | 1320 | 1162 | 881 | 607 | Price ($) |

H_{2} tank 500 bar-200L | Items |
---|---|

6.5 | Mass of H_{2} Stored at 500 bar and 15˚C (kg) |

From −40˚C → 65˚C | Temperature of Use (˚C) |

500 | Maximum Press (bar) |

200 | Inner Volume (Litre) |

188 | Mass of Empty Tank (kg) |

49 × 223 | External Dimensions (cm) |

Polymer liner reinforced with composite material | H_{2} Tank Material |

10 | Life Time (year) |